Technical Program Manager – Infrastructure Engineering

Role overview

Program leadership

• Lead end-to-end technical programmes across GPU orchestration, cluster management, networking, storage, telemetry, and provisioning pipelines.
• Align scope, milestones, dependencies, success metrics, and delivery timelines.
• Drive kickoffs, sprint alignment, risk reviews, and postmortems.
• Coordinate SRE, platform developers, and architects and manage cross-team blockers and risks.
• Communicate programme status, trade-offs, and timelines to infrastructure leadership and stakeholders.

Delivery and technical execution

• Manage roadmaps and release plans for multi-team infrastructure initiatives.
• Implement OKRs, milestones, RACI, risk logs, and SLAs as scalable programme practices.
• Ensure engineering deliverables meet performance, reliability, security, and cost targets.
• Maintain technical understanding of GPU infrastructure and align decisions with NVIDIA Cloud Platform guidance where applicable.
• Facilitate architecture reviews and translate architecture into executable engineering work.
• Coordinate reliability initiatives, incident playbooks, operational runbooks, testing, and rollout strategies.
• Define and track KPIs including uptime, mean time to recovery, and provisioning latency.

Minimum requirements

• Bachelor's degree in computer science, engineering, or equivalent experience.
• At least 5 years of technical programme management experience in infrastructure or platform organisations.
• Hands-on familiarity with at least one of GPU compute stacks, Kubernetes, cluster orchestration, or cloud infrastructure.
• Experience coordinating SRE, platform engineering, and architecture teams on complex multi-quarter programmes.
• Track record delivering projects with multiple engineering teams and external dependencies.
• Excellent communication skills for explaining technical trade-offs and programme status to executives.
• Strong skills in roadmaps, dependency mapping, risk management, and prioritisation.
• Experience defining, monitoring, and improving KPIs and SLAs.
• Comfort with ambiguity and rapid change in a startup environment.
• Experience with incident management, postmortems, and on-call processes.

Nice-to-have qualifications

• GPU orchestration tools such as Device Plugin, MIG, NCCL, or CUDA drivers.
• Kubernetes GPU scheduling frameworks or custom scheduler implementations.
• On-premises or cloud deployment experience.
• Infrastructure-as-code, CI/CD, and observability tooling such as Prometheus, Grafana, or OpenTelemetry.
• Experience in GPU-focused companies, ML infrastructure teams, or HPC environments.

Listed skills

• Service level management, site reliability engineering, project management, product management, cloud infrastructure, incident management, platform engineering, Prometheus, risk management, stakeholder management, and OpenTelemetry.